Enterprise RAG and AI Agents: A Reference Architecture

AI agents and RAG: enterprise reference architectures that ship

Enterprises don't need another demo; they need durable AI agents grounded in their own data. Here's a battle-tested approach to Retrieval-Augmented Generation (RAG) and agent orchestration that supports an enterprise AI strategy and roadmap without spiraling cost or risk.

Reference architecture: from documents to decisions

• Ingestion and normalization: Stream and batch loaders convert PDFs, tickets, wiki pages, and databases into normalized objects. Preserve provenance (URI, owner, timestamp) because auditors will ask for it.
• Chunking and enrichment: Semantic chunking (adaptive lengths based on headings and embeddings) beats naïve fixed windows. Enrich with entities, tables-as-HTML, and vectorizable images (Vision models) to widen recall.
• Indexing: Use hybrid search (BM25 + dense vectors) with filters for tenant, region, and classification. Keep a small keyword index to rescue rare terms and part numbers that embeddings miss.
• Vector store: Choose a service with HNSW or IVF-Flat, per-namespace security, and time-travel snapshots. Store multiple embeddings (general + code) to route by modality.
• LLM gateway: Centralize model access, caching, prompt catalogs, cost controls, and routing by task, PII risk, and latency SLOs.
• Agent and workflow layer: Use a DAG orchestrator for repeatable steps (retrieve → synthesize → validate) and a light planner for tool selection. Keep tools stateless and idempotent.
• Policy and safety: Put policy checks (PII scrubbing, classification, export controls) before and after generation. Redact at retrieval time, not just display time.
• Observability: Log traces with retrievals, prompts, tool calls, costs, and human feedback. You cannot improve what you can't replay.

Tooling that survives production

For retrieval, pair a robust vector store with a reranker (cross-encoder or ColBERT) to boost groundedness. Use an evaluation harness (synthetic Q&A, golden sets, and rejection sampling) and automate regression checks in CI. Add guardrails for schema-constrained outputs (JSONSchema) and policy filters (toxicity, secrets). Feature stores help persist retrieval signals like click-through, which you can fold into reranking.

Patterns beyond FAQ bots

• Procurement agent: Reads contracts and POs, proposes vendors, drafts emails, and opens approvals in your ERP via tool adapters. Retrieval includes vendor risk pages and pricing histories.
• Mobile release copilot: For React Native app development services, an agent reviews crash logs, maps stack traces to known issues, drafts patches, and opens PRs. RAG spans commit history, design docs, and platform notes.
• Field service assistant: Offline-first React Native app retrieves locally cached embeddings and syncs deltas when online. The agent plans steps, cites manuals, and fills work orders.

Build vs. buy and when staff augmentation wins

If you lack specialized LLM, search, and mobile expertise, staff augmentation for software teams accelerates time-to-value without bloating headcount. Bring in platform engineers to harden your LLM gateway, retrieval experts to tune chunking and rerankers, and mobile engineers to embed agents in field apps. Partners like slashdev.io provide vetted remote engineers and agency-grade delivery so you can ship capabilities while building internal muscle.

Common pitfalls (and precise fixes)

• Poor chunking: Overlaps hide citations or bloat tokens. Fix with structure-aware chunkers and adaptive windows; log "coverage" of answers vs. source spans.
• Over-retrieval: Top-50 then LLM "sort" is slow and drifts. Retrieve Top-8, rerank to Top-4, and bound context under a token ceiling.
• No freshness: Monthly re-indexing makes stale answers. Use change-data-capture and hot update paths; add a "last-updated" rule in prompts.
• Evaluation theater: BLEU or ROUGE on long-form is weak. Use groundedness, factuality against citations, and human accept/reject rates per intent.
• Tool spaghetti: Agents calling agents cause loops. Limit concurrency per task, add timeouts, and enforce a planner budget (steps, tokens, dollars).
• Security afterthoughts: Retrieval might leak PII. Classify and tag documents at ingestion; enforce row-level filters at query time.
• Cost creep: Chatty agents destroy margins. Cache embeddings, enable response caching for common queries, and set per-user spend caps.

KPIs that matter

• Retrieval recall@k and precision@k, normalized by corpus size.
• Groundedness and hallucination rate from human or LLM-as-judge, plus citation coverage.
• End-to-end latency p95 and cost per resolved task.
• Human intervention rate and time-to-resolution compared to baseline.
• Change failure rate after agent-generated PRs in mobile pipelines.

Roadmap staged for governance

• Phase 0: Strategy. Align use cases to revenue or risk; define SLOs, data boundaries, and model policy.
• Phase 1: Baseline RAG. Ship a single-intent assistant with eval harness and observability.
• Phase 2: Agent tools. Add planners, tool adapters, and JSON-constrained outputs.
• Phase 3: Enterprise hardening. Multi-tenant security, PII controls, audit trails, shadow mode.
• Phase 4: Scale. Hybrid search tuning, cost optimizations, and offline-capable mobile agents.

Implementation checklist

• Define intents and SLAs; freeze prompt templates by intent.
• Instrument every retrieval with document IDs and scores.
• Adopt hybrid search and a reranker; test on adversarial queries.
• Version data, embeddings, prompts, and models.
• Establish human-in-the-loop for high-risk actions (purchasing, code merge).
• Publish runbooks for drift, cost spikes, and tool failures.

Case vignette

A global manufacturer launched a procurement agent over 3M documents. Hybrid search with a reranker improved recall@5 from 62% to 88%. Groundedness rose to 94%, cost per approved request fell 37%, and cycle time dropped from 5 days to 18 hours. A companion React Native app allowed supervisors to review citations and approve on-site, with offline caching keeping latency low.

The takeaway: anchor agents in measurable retrieval quality, treat orchestration as software engineering, and scale with targeted staff augmentation when speed matters. Your enterprise AI strategy and roadmap should codify these patterns so teams can reuse architecture, tooling, and guardrails-then iterate with confidence.